Fast-Callbacks

Custom Callbacks to extend the Fastai library's functionality
(Feel free to contribute to this project in whatever ways you like !!)

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This package contains generalized callbacks that be used by anyone using the fastai library (V1).
Currently there are 5 callbacks here that I found useful when training models over huge datasets or when the models are too big to increase the batch size over a small number. I plan to keep adding callbacks to this repository as and when I make them for my own use. I'm still and always learning and welcome any changes and feedback to my project.

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1. GradientAccumulator: Accumulates gradients over N iterations before performing an optimizer step.
   This implementation does not solve the subtle issue of BatchNorm layers during gradient accumulation. We simply skip over optimizer steps for N iterations and accumulate gradients before doing the actual step.
   Usage:

    from callbacks import GradientAccumulator
    accumulator = partial(GradientAccumulator, num_iterations=4)
    learn = create_cnn(data, models.resnet18, callback_fns = [accumulator])

2. SaveEveryNIterations: Saves model after every N iterations
   We save all models with the same name as otherwise the models could use up too much memory. Usage:

    from callbacks import SaveEveryNIterations
    saver_callback = partial(SaveEveryNIterations, num_iterations=100, 
                                              save_name="saved_every_100_iterations")
    learn = create_cnn(data, models.resnet18, callback_fns = [saver_callback])

3. StopAfterNIterations : Stops model after N iterations.
   Usage:

    from callbacks import StopAfterNIterations
    stopper = partial(StopAfterNIterations, num_iterations = 17)
    learn = create_cnn(data, models.resnet18, callback_fns = [stopper])

4. ShowResultsEveryNIterations: Shows model results after every N iterations
   Usage:

    from callbacks import ShowResutsEveryNIterations
    results_callback = partial(ShowResutsEveryNIterations, num_iterations=100)
    learn = create_cnn(data, models.resnet18, callback_fns = [results_callback])

5. SkipNIterations: Skips first N iterations while training
   Usage:

    from callbacks import SkipNIterations
    skipper = partial(SkipNIterations, num_iterations=100)
    learn = create_cnn(data, models.resnet18, callback_fns = [skipper])